摘要: |
Pavement data collection technology has been evolving over the past few decades, and increasingly so in recent years. Automated pavement data collection allows agencies to collect data on pavement health at highway speeds, including cracking, rutting, faulting, and roughness. This provides important information to better pavement decision making.
Moving Ahead for Progress in the 21st Century Act (MAP-21) and successive federal legislation requires state departments of transportation (DOTs) to report pavement data at a 0.10-mile interval and establish pavement performance targets. Many state transportation agencies have switched from manual pavement distress survey methods to automated/semi-automated pavement data collection. While agencies gain more data, knowledge, and experience with automated/semi-automated pavement data collection, this migration also brings new challenges. Those challenges include data quality control, data analysis, and decision making, in large part due to the rapidly evolving pavement data collection technology.
Per NCHRP 531, manual condition surveys are conducted by walking or traveling at a slow speed and noting the existing surface distress. Manual surveys may be limited to selected roadway segments (i.e., samples) or span the entire lane area (i.e., 100% survey). Automated condition surveys are conducted using specifically designed vehicles to obtain images and profile data (e.g., IRI, rut depth, faulting) in a single pass at posted speeds. Surface distresses are determined from downward pavement images and post-processed using either semi-automated or fully automated methods.
The objective of this synthesis is to document the experiences, challenges, and state-of-the-practice solutions used by DOTs that are in the midst of transition or that have transitioned to automated/semi-automated pavement data collection processes and summarizing the data for state and federal reporting requirements (e.g., TAMP, MAP-21).
Information will be collected through literature review, a survey of DOTs, and follow-up interviews with selected agencies for the development of case examples. Information gaps and suggestions for research to address those gaps will be identified. |